Big Clustering Research Articles

Using Big Data Analytics to Identify Trends and Group Crimes through Clustering

The incidence of crime in a city presents a challenge in the absence of trend analysis that impacts citizen security. The objective of this research was to analyze and visualize crime trends in the area, using the concepts and fundamentals of Big Data Analytics, Data Mining and Clustering, the problem is addressed with a quantitative approach, using the CRISP-DM process, Principal Component Analysis (PCA) and the K-Means algorithm for clustering. Validation is performed with the Elbow Score and the Average Silhouette method, ensuring the robustness of the data clustering. The results show that crimes against property, such as robbery and theft, are the most frequent. Four crime clusters are identified, each associated with a specific category, providing a detailed view of crime distribution. Comparison with previous studies highlights the effectiveness of Big Data technologies in reducing crime, providing a solid basis for more accurate security strategies.

Research and Application of Big Data Clustering Algorithm based on AI Technology in Cloud Environment

Traditional big data filling algorithms often give inaccurate results due to vulnerabilities to different data types.To solve this problem, this study presents a new big data clustering algorithm powered by AI technology in a cloud environment. The study proposes an advanced Big Data clustering algorithm that leverages AI technology in a cloud environment. It optimizes clustering based on predicted strength using parallel processing. The research focuses on optimizing the clustering algorithm based on the predicted intensity through parallel processing. Experimental results demonstrate that image clustering stability is achieved when the number of clusters exceeds 4, indicating reduced sensitivity to random factors. Although it was not possible to precisely determine the optimal number of clusters, the use of an optimization algorithm showed that at four clusters the prediction intensity reached its peak, ensuring more accurate cluster identification. Through rigorous testing, the optimal number of clusters was determined to be 4. Clustering results show that visitors characterized by certain attributes show higher interest in most columns. This algorithm makes it easier to cluster incomplete large data, improves clustering speed, and improves the accuracy of filling in missing data. Compared to existing methods, this algorithm leverages AI technology in the cloud environment to optimize clustering based on prediction intensity, providing improved accuracy and efficiency during processing in big data management.

PPHOPCM Privacy-Preserving High-order Possibilistic C-Means Algorithm for Big Data Clustering with Cloud Computing

PPHOPCM Privacy-Preserving High-order Possibilistic C-Means Algorithm for Big Data Clustering with Cloud Computing

Optimization enabled LeNet for big data classification using MapReduce framework on COVID-19 data

ABSTRACT The big data clustering is a requisite for generating the data in the digitalised globe. The old-fashioned clustering approaches are not large sized and highly unorganised big data. Thus, to obtain the efficiency of big data clustering, a new architecture is required. This work introduces the Jaya African Vulture Optimization Algorithm-based LeNet (JAVO-based LeNet) for big data classification on COVID-19. The big data clustering is detected by using the LeNet model, which is tuned by the JAVO algorithm. Here, the JAVO algorithm is the integration of the Jaya algorithm and AVOA algorithm. Further, using the Deep Embedded Clustering (DEC) technique for partitioning the smaller data to improve the performance of big data classification. The classification process for COVID-19 is established in the MapReduce (MR) framework. For each mapper, the pre-processing is completed based on Min-Max normalisation and the feature fusion is performed based on the Hellinger distance, which is measured with a deep residual network (DRN). Finally, the classification process for COVID-19 is done by considering the fused data acquired from each mapper with LeNet, which is tuned by the JAVO algorithm. Furthermore, the analysis of the proposed method attains the testing accuracy of 0.948, specificity of 0.939 and sensitivity of 0.950.

Big data clustering algorithm of power system user load characteristics based on K-means and SOM neural network

Big data clustering algorithm of power system user load characteristics based on K-means and SOM neural network

Scalable decision fusion algorithm for enabling decentralized computation in distributed, big data clustering problems

Scalable decision fusion algorithm for enabling decentralized computation in distributed, big data clustering problems

Big data clustering based on spark chaotic improved particle swarm optimization

In recent years, the surge in continuously accelerating data generation has given rise to the prominence of big data technology. The MapReduce architecture, situated at the core of this technology, provides a robust parallel environment. Spark, a leading framework in the big data landscape, extends the capabilities of the traditional MapReduce model. Coping with big data, especially in the realm of clustering, requires more efficient techniques. Meta-heuristic-based clustering, known for offering global solutions within reasonable time frames, emerges as a promising approach. This paper introduces a parallel-distributed clustering algorithm for big data within the Spark Framework, named Spark, chaotic improved PSO (S-CIPSO). Centered on particle swarm optimization (PSO), the proposed algorithm is enhanced with a chaotic map and an efficient procedure. Test results, conducted on both real and artificial datasets, establish the superior performance and quality of clustering results achieved by the proposed approach. Additionally, the scalability and robustness of S-CIPSO are validated, demonstrating its effectiveness in handling large-scale datasets.

An optimized SVM-RFE based feature selection and weighted entropy K-means approach for big data clustering in mapreduce

An optimized SVM-RFE based feature selection and weighted entropy K-means approach for big data clustering in mapreduce

Customer Segmentation Marketing Strategy Based on Big Data Analysis and Clustering Algorithm

Traditional customer segmentation methods cannot obtain more effective information from massive customer data, which affects the formulation of marketing strategies. Based on this, this study constructs a customer segmentation marketing strategy model that integrates support vector machines and clustering algorithms. This model first utilizes support vector machines to segment existing customer data, and then integrates support vector machines and clustering algorithms to construct a customer segmentation model. Finally, simulation experiments are conducted using the dataset. The results show that the model algorithm obtains the optimal solution when the quantity of iterations is 50. Meanwhile, the average error rate of the model algorithm in the customer segmentation process is 6.82%, the average recall rate is 91.28%, and the average profit predicted by the impact strategy developed by the segmentation model is 29.88%, which is 2.53% different from the true value.

Big data clustering using fuzzy based energy efficient clustering and MobileNet V2

Big data analytics (BDA) is a systematic way to analyze and detect various patterns, relationships, and trends in vast amounts of data. Big data analysis and processing require significant effort, techniques, and equipment. The Hadoop framework software uses the MapReduce approach to do large-scale data analysis using parallel processing in order to generate results as soon as possible. Due to the traditional algorithm’s longer execution time and difficulty in processing big amounts of data, this is one of the main issues. Clusters are highly correlated inside each other but are not highly correlated with one another. The technique of effectively allocating limited resources is known as an optimization algorithm for clustering. For processing large amounts of data with several dimensions, the conventional optimization approach is insufficient. By using a fuzzy method, this can be prevented. In this paper, we proposed Fuzzy based energy efficient clustering approach to enhance the clustering mechanism. In summary, Fuzzy based energy efficient clustering introduces a function that measures the distance between the cluster center and the instance, which aids in improved clustering, and we then present the MobileNet V2 model to improve efficiency and speed up computation. To enhance the method’s performance and reduce its time complexity, the distributed database simulates the shared memory space and parallelizes on the MapReduce framework on the Hadoop cloud computing platform. The proposed approach is evaluated using performance metrics such as Accuracy, Precision, Adjusted Rand Index (ARI), Recall, F1-Score, and Normalized Mutual Information (NMI). The experimental findings indicate that the proposed approach outperforms the existing techniques in terms of clustering accuracy.

Big data clustering through fusion of FCM, optimized encoder-decoder CNN, and BiLSTM

Clustering Big Data, as a fundamental component in the processing and analysis of massive datasets, holds crucial importance in addressing complex challenges inherent in handling extensive data sets. Falling within the realm of unsupervised learning methods, the primary objective of clustering is to efficiently organize substantial datasets into homogeneous clusters without relying on pre-existing labels. Our innovative approach seeks to optimize this process by synergistically combining three techniques: the fuzzy C-Means (FCM) methodology, the optimized encoder–decoder CNN model, and the bidirectional recurrent neural network (BiLSTM). This synergy represents a strategic convergence between supervised and unsupervised paradigms. The introduction of BiLSTM is of significant importance, leveraging its capability to sequentially process data from both sides using LSTM cells. This bidirectional approach enhances the understanding of data sequences, a crucial feature in the demanding context of Big Data clustering. Simultaneously, FCM benefits from substantial improvement through the introduction of a function that calculates the separation between the cluster center and the instance, thereby reinforcing the precision of clustering. To optimize performance and reduce computation time, our methodology advocates for the use of the Optimized Encoder–Decoder CNN model. This refined architecture promotes more efficient extraction of data features, thereby enhancing the intrinsic quality of clustering. The rigorous evaluation of our approach revolves around specific data sources, namely fashion MNIST. Performance criteria such as accuracy, adjusted rand index (ARI), and normalized mutual information (NMI) convincingly attest to the remarkable capability of our methodology. In comparative analyses, our approach significantly outperforms existing models, demonstrating its effectiveness and relevance in the complex domain of Big Data clustering.

Design of music signal enhancement system based on big data clustering technology

Design of music signal enhancement system based on big data clustering technology

Study on Mining repeated purchase behavior intention of online consumers based on big data clustering

Study on Mining repeated purchase behavior intention of online consumers based on big data clustering

Analysis of the Integration Teaching Mode of Traditional Music Elements and College Piano under the View of Big Data

Abstract In this paper, the fusion teaching model was constructed, and the clustering of music elements was statistically calculated by calculating the intra-class distance of music elements. The feature vector of big data clustering is extracted, the traditional music element data is partitioned using linear FM signal, and the spatial matrix of traditional music element data is obtained after initializing the clustering center. To form the information flow model of big data time series, the phase space reconstruction analysis method is used to process piano data in nonlinear mapping. To achieve the objective function after clustering, adjust the weights within the fitness function, and then output the optimal program of the integrated teaching model. The results show that the post-test scores of students in the experimental group are higher than those of students in the control group, and the scores of tuning and composition have been improved by 2 and 2.5 points to reach the full score of 10 compared with those of the control group, which demonstrates the validity and feasibility of the fusion teaching mode of traditional music elements and college piano.

Study on mining repeated purchase behaviour intention of online consumers based on big data clustering

Study on mining repeated purchase behaviour intention of online consumers based on big data clustering

Using Big Data to enhance data envelopment analysis of retail store productivity

PurposeThe concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.Design/methodology/approachThe methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.FindingsThe proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.Practical implicationsThe use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.Originality/valueThis article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

A novel efficient Rank-Revealing QR matrix and Schur decomposition method for big data mining and clustering (RRQR-SDM)

Big Data is an emerging technology with enormous potential to develop business and its administration. Due to the enormous volume, efficient data mining and clustering methods are crucial to extracting meaningful insights and patterns from large-scale datasets. Problems may arise from the need to analyze, capture, share, store, and visualize the data. Several methods have already been proposed for mining knowledge from big data. It is practically inefficient or impossible to handle these massive data using the proposed methods in a single machine because big data are frequently acquired from dispersed locations and stored on several machines. Matrix decomposition is one of the critical strategies to retrieve knowledge from diverse, noisy, huge data generated by modern applications and stored in dispersed locations. This study proposes a novel approach called the Rank-Revealing QR Matrix and Schur Decomposition Method (RRQR-SDM) specifically designed for big data mining and clustering tasks. The RRQR-SDM is designed to reveal the rank of the data matrix in a computationally efficient manner by using a modified QR decomposition, eliminating the need for expensive Singular Value Decomposition (SVD) computations. The proposed RRQR-SDM method offers several advantages over existing approaches. Firstly, exploiting the inherent low-rank structure reduces the computational complexity associated with large-scale datasets. By revealing the rank of the input matrix, it enables dimensionality reduction and efficient data compression. Secondly, the Schur decomposition enhances the interpretability of the data by providing a clear separation between the relevant and irrelevant components. This feature makes the RRQR-SDM method particularly suitable for data mining and clustering tasks where identifying the most significant features is essential. To evaluate the performance of the RRQR-SDM method, extensive experiments were conducted on various big data datasets. The results demonstrate that the proposed method outperforms state-of-the-art computational efficiency and clustering accuracy techniques.

Study on the Role of Big Data Technology in Promoting the Transformation of Financial Accounting in the Digital Economy Era

Abstract This paper examines big data technology through the use of big data clustering algorithms and examines the factors that influence the transformation of financial accounting in the digital economy. Separate the clustering center to rectify it, finish accurate clustering of the original data set, and normalize the data. The data center point results are output after dividing the data set, and the final clustering results of the data set are obtained according to the similarity coefficient through the continuous iterative execution process. The results show that the comprehensive scores after the transformation of financial accounting have all risen sharply, and the score of the work content indicator in financial risk avoidance has risen from 4 points before the transformation to 9.5 points. It shows that big data technology can effectively promote the transformation of financial accounting in the digital economy era.

Advancements in big data clustering: methods, applications, and insights

Advancements in big data clustering: methods, applications, and insights

Localized Imagery and Narrative of Chinese Contemporary Oil Painting Based on Big Data

Abstract In this paper, a dataset is obtained by clustering data through a big data clustering algorithm, and a multidimensional state space vector with chaotic trajectories is established by reconstructing the phase space after several iterations to calculate the maximum and minimum similarity distances. The clustering between the localized imagery and narratives of contemporary oil paintings is realized based on the extracted chaotic associative dimensional features. A total of 80 students from an art class at a school were selected to conduct an imagery analysis of the cultural roots of imaginative oil painting narratives, in which the evaluation score of the research class in the imagery of shape and color could reach 10 points. Therefore, the localization of oil painting is a theoretical concept for a Chinese oil painting to go global, which meets the realistic needs and development trend of Chinese oil painting.